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cs.DC2026
Scaling LLM Inference Beyond Amdahl`s Limits via Eliminating Non-Scalable Overheads
Alan Zhao, Cyril Y. He, Wei Xu
Deployers of online LLM services usually seek to maximize cluster-wide performance given a fixed number of GPUs. Tensor parallelism (TP) is necessary to fit modern models but scale…
cs.DC2026
A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability
Ruitao Liu, Xinyang Tian, Shuo Chen +4
Pipeline parallelism is a key technique for scaling large-model training, but modern workloads exhibit runtime variability in computation and communication. Existing pipeline syste…